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Understanding variations in the order picking process: Data-driven and simulation-based approaches
No full textWarehousing plays a pivotal role in modern supply chains, especially in the face of growing e-commerce demands and consumer expectations for fast and reliable delivery. Among warehouse operations, order picking is the most labour-intensive and costly, and, despite technological advances, it is still largely performed manually. Many researchers have focused on improving order picking efficiency, and most of these optimisation models assume idealised, robotic compliance with system-generated instructions. This assumption overlooks one of the primary advantages of employing humans, their autonomy and ability to react to real-time conditions. Surprisingly, little research has focused on understanding the actual behaviour of order pickers, especially by using real-world data to reveal behavioural patterns. This disconnect between model assumptions and real-world behaviour motivates the research in this dissertation. More specifically, this thesis explores the nature and implications of deviations made by order pickers, instances where pickers diverge from prescribed instructions, using data from a real-world warehouse and simulation-based modelling.
The thesis makes two main contributions. First, it develops a structured approach to detect and analyse deviations in order picking, specifically, order deviations (when pickers change the prescribed pick sequence) and time deviations (unexpected variation in task duration). This methodology is applied to a rich dataset from a real-world warehouse, covering over two years of operations and over 500 workers. The analysis uncovers that order deviations occur in almost 5% of the picking tours and that order picking time is heavily right-skewed. The latter implies that while there are limited observations where order pickers perform their tasks faster than expected, there are many occurrences of significant delays. Through a combination of exploratory analysis and mixed-effects regression modelling, the study identifies variables associated with both deviation types and provides new empirical insights into the behavioural patterns of human order pickers.
Second, the thesis examines the operational impact of routing deviations through an agent-based simulation model that reflects modern warehouse environments. Researchers always assume that the optimal routing policy has superior performance over routing heuristics. However, this belief needs to be tested in a realistic environment where order pickers may not always strictly adhere to the routing instructions given to them. In our study, we simulate routing heuristics in storage environments in which they are most effective, a practice common in real-world settings, and compare them to the optimal policy in the same storage environment. The results show that intuitive heuristics, when aligned with their most efficient storage environment, offer comparable performance to optimal routing while reducing cognitive demands on workers. Our findings nuance the belief that optimal routing should always be preferred, and prove that human-centric modelling approaches are essential to recognise human behaviour and still achieve high operational performance.
Together, these contributions advance our understanding of human behaviour during the order picking process and offer practical insights for designing decision support systems that are both efficient and behaviourally realistic. Furthermore, the thesis provides valuable insights for managers by shedding light on how order pickers behave in practice, helping bridge the gap between system design and day-to-day operations
Understanding variations in the order picking process: Data-driven and simulation-based approaches
No full textWarehousing plays a pivotal role in modern supply chains, especially in the face of growing e-commerce demands and consumer expectations for fast and reliable delivery. Among warehouse operations, order picking is the most labour-intensive and costly, and, despite technological advances, it is still largely performed manually. Many researchers have focused on improving order picking efficiency, and most of these optimisation models assume idealised, robotic compliance with system-generated instructions. This assumption overlooks one of the primary advantages of employing humans, their autonomy and ability to react to real-time conditions. Surprisingly, little research has focused on understanding the actual behaviour of order pickers, especially by using real-world data to reveal behavioural patterns. This disconnect between model assumptions and real-world behaviour motivates the research in this dissertation. More specifically, this thesis explores the nature and implications of deviations made by order pickers, instances where pickers diverge from prescribed instructions, using data from a real-world warehouse and simulation-based modelling.
The thesis makes two main contributions. First, it develops a structured approach to detect and analyse deviations in order picking, specifically, order deviations (when pickers change the prescribed pick sequence) and time deviations (unexpected variation in task duration). This methodology is applied to a rich dataset from a real-world warehouse, covering over two years of operations and over 500 workers. The analysis uncovers that order deviations occur in almost 5% of the picking tours and that order picking time is heavily right-skewed. The latter implies that while there are limited observations where order pickers perform their tasks faster than expected, there are many occurrences of significant delays. Through a combination of exploratory analysis and mixed-effects regression modelling, the study identifies variables associated with both deviation types and provides new empirical insights into the behavioural patterns of human order pickers.
Second, the thesis examines the operational impact of routing deviations through an agent-based simulation model that reflects modern warehouse environments. Researchers always assume that the optimal routing policy has superior performance over routing heuristics. However, this belief needs to be tested in a realistic environment where order pickers may not always strictly adhere to the routing instructions given to them. In our study, we simulate routing heuristics in storage environments in which they are most effective, a practice common in real-world settings, and compare them to the optimal policy in the same storage environment. The results show that intuitive heuristics, when aligned with their most efficient storage environment, offer comparable performance to optimal routing while reducing cognitive demands on workers. Our findings nuance the belief that optimal routing should always be preferred, and prove that human-centric modelling approaches are essential to recognise human behaviour and still achieve high operational performance.
Together, these contributions advance our understanding of human behaviour during the order picking process and offer practical insights for designing decision support systems that are both efficient and behaviourally realistic. Furthermore, the thesis provides valuable insights for managers by shedding light on how order pickers behave in practice, helping bridge the gap between system design and day-to-day operations
Quantifying deviations in an order picking process through data-driven analysis
No full textOrder picking is one of the most time-sensitive and cost-critical processes in warehousing, so researchers often focus on this warehouse operation. During this process, orders are retrieved from the warehouse storage to be shipped to customers. It is still applied manually in many warehouses, especially as the current trends, such as decreased product life cycle and increased product differentiation, amplify the need for flexible order picking systems and limit the possibility of automation. In these manual warehouses, picker-to-parts order picking is generally employed. Order pickers travel through the warehouse to collect requested products from the shelves following pre-determined routes. Researchers have developed numerous optimal and heuristic routing policies to guide order pickers efficiently. However, those routing policies often consider a single order picker in isolation or assume that multiple picker' routes are performed independently. In reality, numerous pickers travel through the warehouse simultaneously, and their routes can interfere. This phenomenon, called picker blocking, adds uncertainty to the picker routing problem and can lead to higher picking times than expected.
Using humans (i.e., order pickers) and their specific cognitive and physical skills to fulfill order picking activities makes the process more flexible. For example, when experiencing picker blocking, an order picker can independently decide to wait until the blockage is cleared or deviate from its planned route to avoid the blockage. On the other hand, these human workers' decisions introduce uncertainties in the order picking process. Our goal is to learn about these uncertainties during the order picking process. These insights can be used to include more real-life factors in future research. Numerous aspects can influence the order in which order pickers complete a pick tour and the time it takes to complete the pick tour. We analyze deviations in the order picking process in a broad sense by focusing on both deviations in picking order and in time.
Related work on deviations during the order picking process is limited. First, Glock et al. (2017) use a qualitative approach by conducting surveys to find the types, causes, and consequences. The authors conclude that, although positive effects are possible, the consequences of deviations on order picking efficiency are mainly negative. Second, Elbert et al. (2017) compared optimal and heuristic routing strategies while considering route deviations. To do this, the authors use agent-based simulation to analyze the effects of route deviations on picking time under different routing methods. A range of artificially selected probabilities determines whether a route deviation occurs. They conclude that it is essential to consider route deviations when determining the preferred routing strategy.
We improve warehousing research by analyzing historical data to quantify the prevalence of order picking deviations. Our contribution to order picking research is twofold. Firstly, a methodology to extract insights about order picking deviations from a data set is proposed. Until now, the existence of order picking deviations has only been indicated in survey results. However, a method to extract these insights from real-life data sets is still missing. Secondly, the proposed methodology is applied to a case study to analyze the real-life impact of order picking deviations. Currently, no data-based insights about order picking deviations exist. Implementing real-life factors in order picking research is necessary to make sure that outputs from order picking planning models resemble reality more closely, and it also increases the likelihood of warehouse managers implementing scientific research on warehousing. As the effects of order picking deviations are expected to be mainly negative, the overall warehouse performance will no longer be overestimated. This results in expectations from warehouse managers that are attainable for order pickers and, therefore, higher employee well-being and lower burn-out rates may be achieved.
As order picker deviations may manifest in different ways, the aim is to identify both deviations from the planned pick order of items (e.g., locations in a pick aisle were skipped because the aisle was congested) and deviations from the expected times at which picks are performed (e.g., delays due to picker blocking or pickers taking alternative travel paths). Insights based on an extensive real-life data set will be presented
Quantifying deviations in an order picking process through data-driven analysis
No full textOrder picking is recognized as the most expensive warehouse operation, especially for picker-to-parts systems. Typically, order pickers are assumed to follow a pre-determined route. However, in practice, deviations from this route occur. This phenomenon, known as maverick picking, adds uncertainty to the picker routing problem and can lead to longer picking times than expected.
Existing literature, based on qualitative research and simulation experiments with artificial data, has indicated the detrimental effect of maverick picking on operational performance. However, a quantitative justification based on real-life data is still lacking. Moreover, recent research has shown that failing to comprehend and incorporate human behavior in order picking models may lead to employee discontent, chronic stress, turnover, and burn-out.
Data on individual order pickers is readily available in many warehouses. Therefore, we propose a data-driven approach to quantify the prevalence of maverick picking and to find patterns that cause it. Firstly, this allows analyzing the real-life impact of maverick picking. Secondly, improved order picking planning models considering these new insights can be proposed.
Order picker deviations can manifest in various ways. We aim to identify and quantify both deviations from the planned pick order of items (e.g., skipped locations due to a congested aisle) and deviations from the expected times at which picks are performed (e.g., alternative travel paths)
Quantifying deviations in an order picking process through data-driven analysis
No full textOrder picking is recognized as the most expensive warehouse operation, especially for picker-to-parts systems. Typically, order pickers are assumed to follow a pre-determined route. However, in practice, deviations from this route occur. This phenomenon, known as maverick picking, adds uncertainty to the picker routing problem and can lead to longer picking times than expected.
Existing literature, based on qualitative research and simulation experiments with artificial data, has indicated the detrimental effect of maverick picking on operational performance. However, a quantitative justification based on real-life data is still lacking. Moreover, recent research has shown that failing to comprehend and incorporate human behavior in order picking models may lead to employee discontent, chronic stress, turnover, and burn-out.
Data on individual order pickers is readily available in many warehouses. Therefore, we propose a data-driven approach to quantify the prevalence of maverick picking and to find patterns that cause it. Firstly, this allows analyzing the real-life impact of maverick picking. Secondly, improved order picking planning models considering these new insights can be proposed.
Order picker deviations can manifest in various ways. We aim to identify and quantify both deviations from the planned pick order of items (e.g., skipped locations due to a congested aisle) and deviations from the expected times at which picks are performed (e.g., alternative travel paths)
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